Partial proportional odds model—An alternate choice for analyzing pedestrian crash injury severities

Sasidharan, Lekshmi; Menéndez, Mónica

doi:10.1016/j.aap.2014.07.025

Cited by 98 publications

(58 citation statements)

References 62 publications

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“…También, en otros estudios ( [21], [28]), se determinó que la probabilidad de ocurrencia de una fatalidad o lesión grave, es alta para personas mayores. Este mismo fenómeno, se observa en el presente estudio dado que, las personas mayores de 65 años presentan mayor probabilidad de fallecer, lo cual confirmaría que la edad avanzada es un factor de riesgo de graves lesiones en accidentes de tránsito.…”

Section: Discussionunclassified

“…Una variable explicativa X j que cumple el supuesto de proporcionalidad de odds, tiene efecto uniforme para todos los niveles de la variable respuesta [21], el parámetro correspondiente β j tiene la interpretación usual del logaritmo de OR acumulado, en cada uno de los k − 1 logit acumulados [1]. Así por ejemplo, para un incremento de X j en δ, la razón de odds para X j y X j + δ, obtenidas a partir de la ecuación (17), viene a ser:…”

Section: Interpretaciónunclassified

“…El efecto de una variable explicativa que no cumple el supuesto de proporcionalidad de odds, es explicado mediante el efecto marginal, dado que, el signo de su coeficiente para categorías intermedias de la variable respuesta, no siempre determina la dirección de su efecto [21].…”

Section: Interpretaciónunclassified

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Modelo de Odds Proporcionales Parciales Aplicado al Estudio de la Gravedad de Lesiones por Accidentes de Tránsito

Flores

Palacios

2017

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Resumen: Determinar los factores asociados con la gravedad de lesiones por accidentes de tránsito. Estudio descriptivo correlacional, de diseño observacional, con una muestra aleatoria de 1514 víctimas de accidentes de tránsito en la región de Madre de Dios, entre los años 2010 y 2014, registrados por DGE-MINSA. Se consideraron factores referidos al individuo, al vehículo y al entorno de ocurrencia del accidente. Se ajustó un modelo estadístico de odds proporcionales parciales ordinal. Se identificaron como factores significativamente asociados con la mayor gravedad de las lesiones por accidentes de tránsito (p < 0, 05), la edad de la víctima mayor de 65 años, accidentes involucrando vehículos motorizados grandes y el transporte de heridos al hospital por la policía o serenazgo.Palabras clave: Accidentes de tránsito; Gravedad de lesiones; Modelo de odds proporcionales parciales. Partial Proportional Odds Model Applied to Study of Severity Injuries by Traffic AccidentsAbstract: To determine the risk factors associated with severity of traffic accidents injuries. Descriptive correlational, observational design, with a random sample of 1514 victims of traffic accidents in the Madre de Dios region, from January 2010 through December 2014, retrieved from DGE-MINSA. They are considered factors referred to the persons, the vehicle and the environment of the accident. Partial proportional odds models for ordinal response variable was fit.Were identified as significant risk factors for high levels of severity of injuries caused by traffic accidents (p < 0,05), the victim higher to 65 years old, accidents involving big motorized vehicles and the transport of wounded to hospital by police or segurity guard.Keywords: Road traffic injuries; Severity of injuries; Partial proportional odds model. .0/) que permite el uso no comercial, distribución y reproducción en cualquier medio, siempre que la obra original sea debidamente citada. Para información, por favor póngase en contacto con revistapesqui-

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Modelo de Odds Proporcionales Parciales Aplicado al Estudio de la Gravedad de Lesiones por Accidentes de Tránsito

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2017

Pesquimat

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“…Furthermore, it is not appropriate to assume all of predictors have same effect on different levels of crash severity. PPO model proposed by Peterson and Harrell [21] combines the ordered arrangement in ordinal models and allows certain independent variables to affect different levels of dependent variable differently [22]. Because, PPO models allow a combination of the two ordered and multinomial modelling frameworks, the proportional odds assumption will be relaxed for independent variables that show different effects on different severity level.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Because, PPO models allow a combination of the two ordered and multinomial modelling frameworks, the proportional odds assumption will be relaxed for independent variables that show different effects on different severity level. Sasidharan and Menedez [22] compared PPO models, ordered logit models and multinomial logit models for pedestrian crash injury severities in Switzerland. They found that PPO models have better results than two other models type based on different evaluation criteria.…”

Section: Literature Reviewmentioning

confidence: 99%

A Partial Proportional Odds Model for Pedestrian Crashes at Mid-Blocks in Melbourne Metropolitan Area

et al. 2016

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Abstract. Pedestrian crashes account for 11% of all reported traffic crashes in Melbourne metropolitan area between 2004 and 2013. There are very limited studies on pedestrian accidents at mid-blocks. Mid-block crashes account for about 46% of the total pedestrian crashes in Melbourne metropolitan area. Meanwhile, about 50% of all pedestrian fatalities occur at mid-blocks. In this research, Partial Proportional Odds (PPO) model is applied to examine vehicle-pedestrian crash severity at mid-blocks in Melbourne metropolitan area. The PPO model is a logistic regression model that allows the covariates that meet the proportional odds assumption to affect different crash severity levels with the same magnitude; whereas the covariates that do not meet the proportional odds assumption can have different effects on different severity levels. In this research vehicle-pedestrian crashes at mid-blocks are analysed for first time. In addition, some factors such as distance of crashes to public transport stops, average road slope and some social characteristics are considered to develop the model in this research for first time. Results of PPO model show that speed limit, light condition, pedestrian age and gender, and vehicle type are the most significant factors that influence vehicle-pedestrian crash severity at mid-blocks.

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Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model

Xie

Wong

et al. 2016

J of Advced Transportation

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The present study intended to (1) investigate the injury risk of pedestrian casualties involved in traffic crashes at signalized intersections in Hong Kong; (2) determine the effect of pedestrian volumes on the severity levels of pedestrian injuries; and (3) explore the role of spatial correlation in econometric crashseverity models. The data from 1889 pedestrian-related crashes at 318 signalized intersections between 2008 and 2012 were elaborately collected from the Traffic Accident Database System maintained by the Hong Kong Transport Department. To account for the cross-intersection heterogeneity, a Bayesian hierarchical logit model with uncorrelated and spatially correlated random effects was developed. An intrinsic conditional autoregressive prior was specified for the spatial correlation term. Results revealed that (1) signalized intersections with greater pedestrian volumes generally exhibited a lower injury risk; (2) ignoring the spatial correlation potentially results in reduced model goodness-of-fit, an underestimation of variability and standard error of parameter estimates, as well as inconsistent, biased, and erroneous inference; (3) special attention should be paid to the following factors, which led to a significantly higher probability of pedestrians being killed or sustaining severe injury: pedestrian age greater than 65 years, casualties with head injuries, crashes that occurred on footpaths that were not obstructed/overcrowded, heedless or inattentive crossing, crashes on the two-way carriageway, and those that occurred near tram or light-rail transit stops.Note: RE refers to random effects; the estimate for φ was 2.89 with a 95% Bayesian credible interval (0.60, 8.45). 2022 XU X. ET AL.

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Partial proportional odds model—An alternate choice for analyzing pedestrian crash injury severities

Cited by 98 publications

References 62 publications

Modelo de Odds Proporcionales Parciales Aplicado al Estudio de la Gravedad de Lesiones por Accidentes de Tránsito

Modelo de Odds Proporcionales Parciales Aplicado al Estudio de la Gravedad de Lesiones por Accidentes de Tránsito

A Partial Proportional Odds Model for Pedestrian Crashes at Mid-Blocks in Melbourne Metropolitan Area

Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model

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